Abstract
Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small.- Anthology ID:
- D19-1581
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5758–5765
- Language:
- URL:
- https://aclanthology.org/D19-1581
- DOI:
- 10.18653/v1/D19-1581
- Cite (ACL):
- Jun Saito, Yugo Murawaki, and Sadao Kurohashi. 2019. Minimally Supervised Learning of Affective Events Using Discourse Relations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5758–5765, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Minimally Supervised Learning of Affective Events Using Discourse Relations (Saito et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/D19-1581.pdf